1. Field
The following description relates to a method and an apparatus for estimating a pose of a head for a person.
2. Description of the Related Art
As computers and electronic devices become more prevalent, attempts have been made to develop human computer interfaces to provide more personalization. Head pose estimation is an addressed problem in computer vision. The reason for this is the application potential of an accurate pose estimation system in human computer interaction. Applications in this field include emotion recognition, unobtrusive customer feedback, biological pose correction, and interactive gaze interfaces. Knowledge of the head pose is also useful in other head and face related computer vision applications including surveillance and avatar animation.
Existing methods for head pose estimation are based on three-dimensional (3D) models, machine learning techniques, and/or inferring geometry based on facial features, like eyes, nose, and mouth. These methods face challenges, like person-independent pose estimation, effects of facial expressions, and scalability to estimate poses for a crowd of people.
While 3D techniques give accurate results by constructing a model each time a subject uses the system, this might not be practical in applications, like surveillance and shopping mall displays. Machine learning techniques can better handle different subjects and facial expressions. However, machine learning techniques include challenging training pipelines requiring huge training data, and are computationally expensive during testing. Also, machine learning techniques suffer from tedious alignment issues, sensitivity to illumination, and non-scalability to estimate poses for multiple subjects.
As a result, the existing methods of estimating a head pose are not capable of dealing with agile motion and mitigating drift. Due to these drawbacks, the existing methods obtain results that are not very efficient.